def test_compute_cell_mask_between_nucleus_centroids(self):
image_set = ImageSet(self.repo, path_list=['mrna/actn2/immature/'])
nuc_dist, nucs_dist, cell_masks, nucs_pos = image_set.compute_cell_mask_between_nucleus_centroids()
self.assertEqual(np.sum([754, 483, 526]), np.sum(nuc_dist))
self.assertEqual(len([754]) + len([483, 526]), len(nucs_dist[0]) + len(nucs_dist[1]))
for nuc_pos in nucs_pos:
if len(nuc_pos) == 1:
self.assertEqual(nuc_pos[0],[110, 864])
else:
self.assertEqual(nuc_pos[0], [154, 637])
self.assertEqual(nuc_pos[1], [637, 1163])
secondary_fp = pathlib.Path(dataset_root_fp,
constants.dataset_config['SECONDARY_FILE_NAME'])
analysis_repo = H5RepositoryWithCheckpoint(repo_path=primary_fp,
secondary_repo_path=secondary_fp)
z_line_spacing = constants.analysis_config['Z_LINE_SPACING']
band_n = constants.analysis_config['STRIPE_NUM']
molecule_type = ['/mrna']
genes = ['actn2-mature', 'gapdh-mature', 'actn2-immature']
# figure 8C degree of clustering for muscle data
for g in genes:
[gene, timepoint] = g.split("-")
image_set = ImageSet(analysis_repo, [f"{'mrna'}/{gene}/{timepoint}/"])
nuc_dist, nucs_dist, cell_masks, nucs_pos = image_set.compute_cell_mask_between_nucleus_centroids(
)
# compute histogram mod
hx, hy, _ = plt.hist(nuc_dist)
bin_max = np.where(hx == hx.max())[0]
hist_mod = hy[bin_max]
if len(hist_mod) > 1:
hist_mod = np.mean(hist_mod)
# compute density by stripe and build spline wave graph
image_counter = 0
for im in image_set.get_images():
nucleus_mask = im.get_nucleus_mask()
nucleus_centroids = im.get_multiple_nucleus_centroid()
spots = im.get_spots()
z_lines = im.get_z_lines_masks()
